It is worth noting that several works have explored low-complexity 3D segmentation via 2D representations of point clouds (e.g., [1]}, [2]}). Instead of processing 3D points directly, point clouds are firstly projected into a set of 2D range images (RIs) [one can see Fig. REF  (c)] and then segmented. Note that each pixel of RIs contains the range from each point to the center of the sensor's coordinate system. It is natural to consider this projection method for our work, where point clouds are transformed into RIs and then fed into VFI models to generate the interpolated frame. Nevertheless, naively applying these techniques usually leads to sub-optimal frame interpolation performance. It is likely due to the fact that the inherited characteristics of RIs differ from the normal color images. For example, the feature distribution of the color images is basically constant at their different spatial locations, while RIs are in the opposite case [2]}. Meanwhile, the feature extractors adopted in the existing VFI approaches are typically standard convolutions, which have the spatial sharing nature, i.e., the parameters of convolutions are shared across the whole input. Therefore, the feature extractors may not be effective to handle the RIs, leading to relatively low frame interpolation performance.
